Multimodal sensor fusion in the latent representation space
Artificial Intelligence
2025-05-29 v1 Human-Computer Interaction
Machine Learning
Signal Processing
Abstract
A new method for multimodal sensor fusion is introduced. The technique relies on a two-stage process. In the first stage, a multimodal generative model is constructed from unlabelled training data. In the second stage, the generative model serves as a reconstruction prior and the search manifold for the sensor fusion tasks. The method also handles cases where observations are accessed only via subsampling i.e. compressed sensing. We demonstrate the effectiveness and excellent performance on a range of multimodal fusion experiments such as multisensory classification, denoising, and recovery from subsampled observations.
Cite
@article{arxiv.2208.02183,
title = {Multimodal sensor fusion in the latent representation space},
author = {Robert J. Piechocki and Xiaoyang Wang and Mohammud J. Bocus},
journal= {arXiv preprint arXiv:2208.02183},
year = {2025}
}
Comments
Under review for Nature Scientific Reports